[adgjlsfhk1]

Single thread CPU Linear algebra is the bottleneck of most small systems, so if you can't do that right, you are going to have problems. If you don't believe the benchmark, feel free to run them yourself.

That said, Jax also has bigger issues in it's handling of higher derivatives. Currently, it only supports a few types of jacobians, and the ones it is missing include all the sparse methods that can make your code orders of magnitude faster. https://jax.readthedocs.io/en/latest/notebooks/autodiff_cook.... DifferentialEquations, on the other hand can do automatic sparsity detection https://diffeq.sciml.ai/stable/tutorials/advanced_ode_exampl....

[dekhn]

This post is about Big Simulations, not small systems. Like, hundreds to thousands of cores wiht parameters that don't fit in RAM on a single machine.

I am sure the benchmark produces the numbers the author says, but it's not measuring something useful to the posters of this simulation.